Stochastic modelling of cell differentiation networks from partially-observed clonal tracking data

Abstract: Motivation: Clarifying how hematopoietic stem cells differentiate into mature cell types is important for understanding how they attain specific functions and offers the potential for therapeutic manipulation. Over the past decades, clonal tracking has proven to be capable of unveiling population dynamics and hierarchical relationships in vivo. For this reason, clonal tracking studies are required for safety and long-term efficacy assessment in gene therapy. However, many standard clonal tracking studies consider only a subset of cell-types and are subject to noise. Results: In this work, we propose a stochastic framework that investigates the dynamics of cell differentiation from typical clonal tracking data subject to measurement noise, false-negative errors, and systematically unobserved cell types. Our framework is based on stochastic reaction networks combined with extended Kalman filtering and Rauch-Tung-Striebel smoothing. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies to better understand clonal reconstitution dynamics. Availability: The stochastic framework is implemented in the R package Karen which is available for download at https://github.com/delcore-luca/Karen. The code that supports the findings of this study is openly available at https://github.com/delcore-luca/CellDifferentiationNetworks.